Online Communication as a Potential Travel Medicine Research Tool: Analysis of Messages Posted on the TravelMed Listserv
Bibliographic record
Abstract
BACKGROUND: Access to the Internet and electronic mail has created opportunities for online discussion that can facilitate medical education and clinical problem solving. Research into the use of these information technologies is increasing and the analysis of these tools can support and guide the activities of professional organizations, including educational endeavors. OBJECTIVE: The initial objective was to analyze patterns of information exchange on the International Society of Travel Medicine's (ISTM) travel health electronic mailing list related to a specific area of society interest. Secondary objectives included the analysis of listserv use in relation to subscriber demographics and rates of participation to support travel health educational activities. METHODS: This study examined the use of the ISTM TravelMed listserv over an 8-month period from January 1, 2006, to July 31, 2006. Descriptive data analysis included TravelMed user demographics, the type of posting, the topic and frequency of postings, and the source of information provided. RESULTS: During the study period, 911 (47%) of the eligible ISTM members subscribed to the TravelMed listserv. About 369 of these subscribers posted 1,710 individual messages. About 1,506 (88%) postings were educational; 207 (12%) postings were administrative. A total of 389 (26%) of the educational postings were primary queries and 1,120 (74%) were responses, with a mean string length of 2.9 responses per query (range: 1-51). Twenty participants contributed 40% of the educational postings. The topics with the most frequent postings were vaccines and vaccine-preventable diseases (473/31%) and malaria (258/17%). Postings focused on special populations, including pregnant women or immigrants, comprised a total of 14 postings (<1%). CONCLUSIONS: During the study period, a limited number of ISTM members (19%) authored postings on the listserv. Regular discussion centered on a limited number of recurring topics. The analysis provides several opportunities for the support of educational initiatives, clinical problem solving, and program evaluation.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.009 | 0.004 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.002 | 0.000 |
| Bibliometrics | 0.003 | 0.004 |
| Science and technology studies | 0.000 | 0.001 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.001 | 0.000 |
| Research integrity | 0.000 | 0.003 |
| Insufficient payload (model declined to judge) | 0.001 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".